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6. Conclusion


Given the vast amount of data available online, the visual analysis of social networks has become exciting but also challenging. Tools are required to scale to handle very large networks whereas traditional node-link representations do not scale very well. Without such visualization tools, statistical tools remain the most reliable approach to analyze large social networks. While statistical tools help answering a vast number of questions and validate hypotheses, they do not support the exploration process very well. Supporting this exploration process and helping analysts discover insights about the data and answering questions they did not even know they had is the goal of information visualization [42].

In this chapter, we presented a number of recent works to visually explore social networks. These novel information visualization techniques open a new era for the exploratory analysis of social networks. They allow scaling to larger networks and provide powerful communication means.


We initiated this chapter by presenting a number of techniques to help node-link diagrams scale to larger networks. We highlighted the familiarity of these representations and attempted to describe when these representations are more appropriate. However, node-link diagrams suffer from important readability problems [36]. For this reason, we presented a set of novel techniques based on adjacency matrix representations [43]. We showed that matrix-based representations can scale to larger networks and provide insightful overviews. Through the chapter, we stressed the necessity to reorder their rows and columns and learn to decode their visual patterns.
Information visualization advocates for the use of multiple representations; providing analysts with multiple perspectives on their datasets and interactive tools to manipulate them. Following this philosophy, we combined both node-link diagrams and matrix representations with MatrixExplorer [37] and presented a number of techniques to interact with these representations. To go a step further, we presented novel representations merging node-link diagrams and matrices: MatLink [38], overcoming the problem of paths finding in matrices, and NodeTrix [39], improving the readability of dense clusters in node-link diagrams. This set of visualization techniques presented in this chapter aims at helping analysts explore social networks, raising novel questions about a particular dataset and discovering new insights.
A concrete example of exploratory analysis using matrix-based representations is presented in [44]. In this case study, we reported insights on the scientific collaboration in the field of HCI. Figure 19 presents a few visualizations extracted from this case study. Learning to decode specific patterns in matrices can lead to interesting discoveries and quickly attract the attention of an analyst on salient part of a network.
While we addressed the challenge of visualizing larger and denser social networks, other challenges remain. In particular, merging exploratory techniques with model-based techniques remains to be done to validate hypothesis once they are found visually or explore discrepancies from an expected model.



C

D

B

A

Figure 19. Matrix-based representations depicting the collaboration network of researchers in information visualization. The matrix shows a central actor (Shneiderman) as well as a group of researchers collaborating strongly with each other (PARC). The NodeTrix view shows different patterns of collaboration. A shows a clique, B shows two cliques with three actors bridging them. Both A and B tend to be collaboration patterns of research companies, C shows a standard collaboration pattern for university professors (they collaborate with many students who rarely collaborate with each other) and D shows a hybrid version of these two patterns.



The same patterns are visible in Figure 16.

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